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Record W3025295486 · doi:10.1149/ma2020-01522839mtgabs

Electrochemical Reactivity of Flavonoids and Flavonoid-Metal Ion Complexes with the Superoxide Anion Radical

2020· article· en· W3025295486 on OpenAlexaff
Tyra Lewis, Sanela Martić

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsTrent University
Fundersnot available
KeywordsSuperoxideChemistryFlavonoidReactive oxygen speciesAntioxidantCyclic voltammetryContext (archaeology)Oxidative stressElectrochemistryInorganic chemistryBiochemistryElectrodeEnzymeBiology

Abstract

fetched live from OpenAlex

Reactive oxygen species (ROS), such as the superoxide anion radical, O2ׄ-, are by-products of aerobic cellular metabolism.1,2 ROS may serve as signaling molecules in some cellular processes, depending on the balance of ROS produced and ROS scavenging mechanisms at work.1 However, they may also lead to undesirable reactions in biological systems.1 Specifically, an excess production of ROS can cause oxidative stress, contributing to cellular death and the development of some pathological diseases, like cancer and Parkinson’s disease.1-3 Dietary flavonoids serve as antioxidant supplements to fight against ROS-induced damages in the system.4 Flavonoids are also known to play a role as metal ion chelators, in a biological environment, which subsequently modulates the antioxidant capacity of the flavonoid compound.1,4 Hence, greater understanding of flavonoid chemistry with ROS in the context of metal ions is needed. In this work, a previously established three-electrode electrochemical assay was used to evaluate the reactivities of flavonoids and flavonoid-metal ion complexes with electrochemically-generated superoxide anion radical, O2ׄ-.5 Cyclic voltammetry (CV) was used with a glassy carbon working electrode, a Pt wire counter electrode and Ag/AgNO3 reference electrode immersed in DMF, in this assay. CV was used to measure the electrochemical signal associated with O2ׄ- and its modulation in the presence of a specific flavonoid and metal ion (Cu(II), Zn(II), Fe(III), Mn(II), Cd(II)). Data indicated that flavonoids depleted a signal associated with O2ׄ- and that this reactivity was regulated in the presence of metal ions. References 1 P. Sharma, A. B. Jha, R. S. Dubey, and M. Pessarakli, J. Bot., 2012, 1–26 (2012). 2 M. P. Murphy, Biochem. J., 417, 1–13 (2008). 3 C. L. Bourvellec, D. Hauchard, A. Darchen, J.-L. Burgot, and M.-L. Abasq, Talanta, 75, 1098–1103 (2008). 4 K. E. Heim, A. R. Tagliaferro, and D. J. Bobilya, J. Nutr. Biochem., 13, 572–584 (2002). 5 N. L. Zabik, S. Anwar, I. Ziu, and S. Martic-Milne, Electrochim. Acta, 296, 174–180 (2019).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.190
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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